• What is your subject/ your profession?
• How did you get to know R?
• What is your motivation to learn more?

• up to 2 hours of online-class from Monday to Tuesday
• homework, to be done until next day
• 1 hour for questions in the afternoon or evening via video-call, e-mail, stud-ip
• if you want you can send me your homework until 6 pm
• small project after the course (approx. 5 hours, or 35 hours)

• created in 1992
• extends the S programming language
• S was commercial, R is free software under the GNU license
• implemented by Ross Ihaka and Robert Gentleman
• since 1997 supported by the R Core Group
• Aim: A programming language by Statisticians for Statisticians

[W]e wanted users to be able to begin in an interactive environment, where they did not consciously think of themselves as programming. Then as their needs became clearer and their sophistication increased, they should be able to slide gradually into programming, when the language and system aspects would become more important.”

Low level:

• Machine Code
• Assembly

High level(s):

• 3rd generation, e.g. C, C++, Java
• 4th generation, e.g. R, Python, MatLab
• 5th generation/ artificial languages

• it is considered object oriented
• but take care, object orientation in R is nasty!
• imperative (you implement the "how" not the "what")
• functional (you put instructions in functions, you can pass functions to functions)
• not strongly typed (you do not know the type of an object before running your code)

R is great for

• statistical modelling
• mathematical calculations, vector arithmetic, …
• data visualisations
• websites
• reports

You might want to choose an other programming language for

• native applications
• extensive calculations
• agent-based models

1. If you type code directly in the console, will the objects you create occur in the environment frame? Could you imagine any problems occuring because of this?

2. When I want to close R, there is this pop-up window asking 'Save workspace image to ~/Documents/informatica/R/.RData?'. What does this mean? Is this useful?

1. Yes. All code you run will result in objects saved in the environment. You can click on the small broom symbol to delete all objects. A problem occurs when you change objects, but you do not consider that in your script.

2. When you save the workspace, all object you created within the environment will be retained. The workspace is saved to a hidden file called .RData, saved to your current working directory / project directory. Advantages of saving the workspace: You start at the same point, with the same objects you had, when you last closed your session. You do not need to recompute your objects, which can be time-intensive. Disadvantages: Your working directory is not in a clean state, when you start. You write code, so that you are able to reproduce your calculation, so no need to store objects. There are other ways to store objects, which take a long time to compute (-> rds-files). ")

firstname <- "Diren"
surname <- "Senger"
name <- paste(firstname, surname) # Concatenation
name

## [1] "Diren Senger"

class(name) # Check the type

## [1] "character"

age <- as.character(25) # Casting
class(age)

## [1] "character"

a <- 1
b <- 2
c <- a + b
c

## [1] 3

d <- b*c/a
d

## [1] 6

e <- d^2
e

## [1] 36

f <- as.integer(3.14)
f

## [1] 3

a <- TRUE
b <- FALSE

# or
a|b

## [1] TRUE

# and
a&b

## [1] FALSE

# not
!a

## [1] FALSE

4 == 5

## [1] FALSE

4 < 5

## [1] TRUE

4 >= 4

## [1] TRUE

somevariable <- NULL
is.null(somevariable)

## [1] TRUE

# a simple numeric vector
myvector <- c(1,4,6)
myvector

## [1] 1 4 6

# a vector of type character
myfamily <- c("Paula", "Ali", "Emma")
myfamily

## [1] "Paula" "Ali"   "Emma"

# get the first element of a vector
myfamily[1]

## [1] "Paula"

# apply function to all items:
myvector + 1

## [1] 2 5 7

paste(myfamily, "Meier")

## [1] "Paula Meier" "Ali Meier"   "Emma Meier"

# concatenate vectors:
longervector <- c(myvector, 5:7)
longervector

## [1] 1 4 6 5 6 7

# create a sequence
odd <- seq(from = 1, to = 10, by = 2)
odd

## [1] 1 3 5 7 9

# create a boring sequence
boring <- rep(1, 10)
boring

##  [1] 1 1 1 1 1 1 1 1 1 1

fac <- factor(c("good", "better", "best"))
levels(fac)

## [1] "best"   "better" "good"

nlevels(fac)

## [1] 3

result <- list(data="fancy test data", mean=5, sd=1.32)
result

## $data
## [1] "fancy test data"
## 
## $mean
## [1] 5
## 
## $sd
## [1] 1.32

and annoying double brackets

myList <- list(smallLetters=letters[1:10], 
               capitalLetters=LETTERS[1:10], 
               numbers=1:5)
myList

## $smallLetters
##  [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j"
## 
## $capitalLetters
##  [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J"
## 
## $numbers
## [1] 1 2 3 4 5

and annoying double brackets

# Accessing multiple elements
myList[1:2]

## $smallLetters
##  [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j"
## 
## $capitalLetters
##  [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J"

and annoying double brackets

# Accessing single elements
myList[2][2]

## $<NA>
## NULL

myList[[2]][2]

## [1] "B"

myList[[1:2]]

## [1] "b"

m <- matrix(data=1:12, nrow = 3, ncol = 4)
m

##      [,1] [,2] [,3] [,4]
## [1,]    1    4    7   10
## [2,]    2    5    8   11
## [3,]    3    6    9   12

m

##      [,1] [,2] [,3] [,4]
## [1,]    1    4    7   10
## [2,]    2    5    8   11
## [3,]    3    6    9   12

# Element-wise operations
m * 0.5

##      [,1] [,2] [,3] [,4]
## [1,]  0.5  2.0  3.5  5.0
## [2,]  1.0  2.5  4.0  5.5
## [3,]  1.5  3.0  4.5  6.0

m

##      [,1] [,2] [,3] [,4]
## [1,]    1    4    7   10
## [2,]    2    5    8   11
## [3,]    3    6    9   12

# Element-wise operations
m * c(1,2,3)

##      [,1] [,2] [,3] [,4]
## [1,]    1    4    7   10
## [2,]    4   10   16   22
## [3,]    9   18   27   36

m

##      [,1] [,2] [,3] [,4]
## [1,]    1    4    7   10
## [2,]    2    5    8   11
## [3,]    3    6    9   12

# Matrix multiplication
m %*% c(1,2,3,4)

##      [,1]
## [1,]   70
## [2,]   80
## [3,]   90

Good explanation of Matrix multiplication: Eli Bendersky's website

# Creating a data frame
myfamily <- c("Paula", "Ali", "Emma")
family.frame <- data.frame(index = 1:3, 
                           firstname = myfamily, 
                           surname = rep("Meier", 3))

df <- read.table("beedatasmall.csv")

## Error in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, : line 2 did not have 2 elements

df <- read.table("beedatasmall.csv", sep = ",")

df <- read.table("beedatasmall.csv", sep = ",", header = T)

df <- read.csv("beedatasmall.csv")

df <- read.csv("sampleData.csv")

df <- read.csv("sampleData.csv", sep=";")

df <- read.csv("beedata.csv")
str(df)

## 'data.frame':    171299 obs. of  10 variables:
##  $ time     : num  1.57e+18 1.57e+18 1.57e+18 1.57e+18 1.57e+18 ...
##  $ hive     : int  4 4 13 4 4 4 4 4 4 4 ...
##  $ h        : num  NA NA 59.1 NA NA ...
##  $ t_i_1    : num  27.2 27.2 22.9 27.2 27.2 ...
##  $ t_i_2    : num  26.4 26.4 22.2 26.4 26.4 ...
##  $ t_i_3    : num  23.4 23.5 23 23.5 23.5 ...
##  $ t_i_4    : num  23.5 23.5 21.8 23.6 23.6 ...
##  $ t_i_5    : num  23.2 23.2 22.7 23.2 23.2 ...
##  $ t_o      : num  21.6 21.6 22.9 21.6 21.6 ...
##  $ weight_kg: num  NA NA 10.8 NA NA ...

E.g. converting kg to g:

df$weight_g <- df$weight_kg*1000

Marking high temperature values:

df$highTemp <- df$t_i_1>25

Nanoseconds to seconds:

df$timeSeconds <- df$time/1000000000

Dealing with the timestamp

df$time <- as.POSIXct(df$timeSeconds, origin="1970-01-01")

df[choose_rows, choose_columns]

with some boolean expressions

Select and plot data from hive 4:

df.4 <- df[df$hive==4,]
plot(df.4$time, df.4$t_o, ylim=c(0,40))

Select only the first 1000 lines:

df.4.sub <- df.4[1:1000,]
plot(df.4.sub$time, df.4.sub$t_o, ylim=c(0,40))

Delete columns: / Choose only some columns

names(df)

##  [1] "time"        "hive"        "h"           "t_i_1"       "t_i_2"      
##  [6] "t_i_3"       "t_i_4"       "t_i_5"       "t_o"         "weight_kg"  
## [11] "weight_g"    "highTemp"    "timeSeconds"

df.some <- df[, c(1,2,10)]

You can also use:

df.same <- df[, c("time", "hive", "weight_kg")]

You can also use:

df.or <- df[, -c(3:9,11:13)]

• summary
• head
• tail
• sum
• mean

a <- 19
if(a >= 18){
  print("Yes, you are allowed to see this content.")
}

## [1] "Yes, you are allowed to see this content."

goodWeather <- TRUE
if (goodWeather){
  print("Let's go to the beach!")
} else{
  print("Let's eat pizza.")
}

## [1] "Let's go to the beach!"

do.you.want.this <- "chocolate"
#do.you.want.this <- "cookies"
#do.you.want.this <- "carrots"
if (do.you.want.this == "chocolate"){
  print("Yes, I love chocolate!")
} else if (do.you.want.this == "cookies"){
  print("Yes, if they are with chocolate.")
} else {
  print("Hm, not sure. Do you have chocolate?")
}

## [1] "Yes, I love chocolate!"

library(lubridate)
birthday <- as.POSIXct("2019-08-06 10:50:18")
what.do.you.want.to.know <- "year"
#what.do.you.want.to.know <- "month"
#what.do.you.want.to.know <- "chocolate"
if (what.do.you.want.to.know == "year"){
  print(year(birthday))
} else if (what.do.you.want.to.know == "month"){
  print(month(birthday))
} else {
  print("Sorry, what do you want to know?")
}

## [1] 2019

raincoat <- T
rain <- T
if(raincoat){
  if(rain){
    print("dry")
  } else{
    print("useless raincoat")
  }
} else{
  if(rain){
    print("wet")
  } else{
    print("dry")
  }
}

## [1] "dry"

myfamily <- list(Paula=31, Ali=29, Emma=5)
age <- 0
for (i in 1:length(myfamily)){
  age <- age + myfamily[[i]]
}
age

## [1] 65

ages <- c(31, 29, 5)
total.age <- 0
for(i in ages){
  total.age <- total.age + i
}
total.age

## [1] 65

for (i in 1:ncol(df)){
  name <- names(df)[i]
  colclass <- class(df[,i])
  print(paste(name, ":", colclass))
}

## [1] "time : POSIXct" "time : POSIXt" 
## [1] "hive : integer"
## [1] "h : numeric"
## [1] "t_i_1 : numeric"
## [1] "t_i_2 : numeric"
## [1] "t_i_3 : numeric"
## [1] "t_i_4 : numeric"
## [1] "t_i_5 : numeric"
## [1] "t_o : numeric"
## [1] "weight_kg : numeric"
## [1] "weight_g : numeric"
## [1] "highTemp : logical"
## [1] "timeSeconds : numeric"

df <- read.csv("beedata.csv")
print("mean of h:")
mean(df$h)
print("mean of t_i_1:")
mean(df$t_i_1)
print("mean of weight_kg:")
mean(df$weight_kg)
print("missing h values:")
sum(is.na(df$h))
print("missing t_i_1 values:")
sum(is.na(df$h))
print("missing weight_kg values:")
sum(is.na(df$h))

to.analyse <- c("h", "t_i_1", "weight_kg")
for(i in 1:length(to.analyse)){
  print(paste("mean of", to.analyse[i], ":"))
  print(mean(to.analyse[i]))
  print(paste("missing", to.analyse[i], "values:"))
  print(sum(is.na(df[,to.analyse[i]])))
}

apply(df, 2, class)

##        time        hive           h       t_i_1       t_i_2       t_i_3 
## "character" "character" "character" "character" "character" "character" 
##       t_i_4       t_i_5         t_o   weight_kg    weight_g    highTemp 
## "character" "character" "character" "character" "character" "character" 
## timeSeconds 
## "character"

lapply(df, class)

## $time
## [1] "POSIXct" "POSIXt" 
## 
## $hive
## [1] "integer"
## 
## $h
## [1] "numeric"
## 
## $t_i_1
## [1] "numeric"
## 
## $t_i_2
## [1] "numeric"
## 
## $t_i_3
## [1] "numeric"
## 
## $t_i_4
## [1] "numeric"
## 
## $t_i_5
## [1] "numeric"
## 
## $t_o
## [1] "numeric"
## 
## $weight_kg
## [1] "numeric"
## 
## $weight_g
## [1] "numeric"
## 
## $highTemp
## [1] "logical"
## 
## $timeSeconds
## [1] "numeric"

sapply(df, class)

## $time
## [1] "POSIXct" "POSIXt" 
## 
## $hive
## [1] "integer"
## 
## $h
## [1] "numeric"
## 
## $t_i_1
## [1] "numeric"
## 
## $t_i_2
## [1] "numeric"
## 
## $t_i_3
## [1] "numeric"
## 
## $t_i_4
## [1] "numeric"
## 
## $t_i_5
## [1] "numeric"
## 
## $t_o
## [1] "numeric"
## 
## $weight_kg
## [1] "numeric"
## 
## $weight_g
## [1] "numeric"
## 
## $highTemp
## [1] "logical"
## 
## $timeSeconds
## [1] "numeric"

a <- 2
while (a<1000){
  a <- a^2
  print(a)
}

## [1] 4
## [1] 16
## [1] 256
## [1] 65536

letters <- letters[]
repeat{
  i <- as.integer(runif(1,1,26))
  letter <- letters[i]
  print(letter)
  if(letter=='o'){
    break
  }
}

## [1] "o"

a <- 0.5
repeat{
  a <- a - sqrt(a)
  print(a)
  if(sqrt(a) > a){
    break
  }
}

## [1] -0.2071068

## Warning in sqrt(a): NaNs produced

## Error in if (sqrt(a) > a) {: missing value where TRUE/FALSE needed

https://me.me/i/while-not-edge-do-run-run-while-not-edge-5b875ee64adb4e939ee50ad394309509

When do you use for? When do you use while? Can you think of examples?

• if you do not know the number of iterations needed, use while
• else, always use for!
• it is easier to create a while-loop that never stops, than creating a for-loop that never stops.

myPlus <- function(a, b){
  return(a + b)
}
myPlus(1,2)

## [1] 3

myPlus <- function(a=1, b=1){
  return(a + b)
}
myPlus(1,2)

## [1] 3

myPlus()

## [1] 2

dataframe.info <- function(df){
  cells.count <- ncol(df)*nrow(df)
  return(list(columns=ncol(df), rows=nrow(df), cells= cells.count))
}
dataframe.info(df)

## $columns
## [1] 13
## 
## $rows
## [1] 171299
## 
## $cells
## [1] 2226887

funny.words <- function(s = "summer", count = 3){
  s = "summer"
  s1 <- ""
  for(i in 1:2){
    for(i in 1:count){
      print(substr(s, i, i))
      s1 <- paste(s1, substr(s, i, i), sep = "")
    }
  }
  return(s1)
}
funny.words()

## [1] "s"
## [1] "u"
## [1] "m"
## [1] "s"
## [1] "u"
## [1] "m"

## [1] "sumsum"

funny.words("banana", 5)

## [1] "s"
## [1] "u"
## [1] "m"
## [1] "m"
## [1] "e"
## [1] "s"
## [1] "u"
## [1] "m"
## [1] "m"
## [1] "e"

## [1] "summesumme"

• If you click on Next, R will execute the next line. (e.g. line 6)
• If you click on the second button, R will step in the current function call, so it will basically jump into an other function. (e.g. into the print function)
• If you click on the third button, R will execute the rest of the current function or loop. (e.g. line 6 and 7)
• If you click on “continue”“, R will run until we come across the next breakpoint. (e.g. in the next round of the loop or in line 10)
• If you click on “Stop”, R will exit the debug mode.

Use the debugging options to find all mistakes in the funny.words function.

• many, many libraries
• we are going to look at base, ggplot and plotly

df.4 <- df[df$hive==4,]
# plot temperature outside
plot(df.4$time, df.4$t_i_3)

# plot temperature outside
plot(df.4$time, df.4$t_i_3, 
     ylim=c(0,40)) # added this

# plot temperature outside
plot(df.4$time, df.4$t_i_3, 
     ylim = c(0,40), 
     xlab = "Time (2019)",  # added this
     ylab = "Temperature within hive", # added this
     main = "Sensor measurements") # added this

# plot temperature outside
plot(df.4$time, df.4$t_i_3, 
     ylim = c(0,40), 
     type = "b", # added this
     lty = 1, # added this
     xlab = "Time (2019)", 
     ylab = "Temperature within hive", 
     main = "Sensor measurements")

• “p”: Points
• “l”: Lines
• “b”: Both

Source: http://www.sthda.com/english/wiki/line-types-in-r-lty

df.4 <- df.4[df.4$t_i_3>5&df.4$t_i_3<40,] # added this
# plot temperature outside
plot(df.4$time, df.4$t_i_3, 
     ylim = c(0,40), 
     type = "b",
     lty = 1,
     xlab = "Time (2019)", 
     ylab = "Temperature within hive", 
     main = "Sensor measurements")

# plot temperature outside
plot(df.4$time, df.4$t_i_3, 
     ylim = c(0,40), 
     type = "b",
     lty = 1,
     pch = 4, # added this
     xlab = "Time (2019)", 
     ylab = "Temperature within hive", 
     main = "Sensor measurements")

Source: http://www.sthda.com/english/wiki/r-plot-pch-symbols-the-different-point-shapes-available-in-r

# plot temperature outside
plot(df.4$time, df.4$t_i_3, 
     ylim = c(0,40), 
     type = "b",
     lty = 1,
     pch = 4,
     xlim = as.POSIXct(c("2019-08-08", "2019-08-09")), # added this
     xlab = "Time (2019-08-08)", 
     ylab = "Temperature within hive", 
     main = "Sensor measurements")

# plot temperature outside
plot(df.4$time, df.4$t_i_3, 
     ylim = c(0,40), 
     type = "b",
     lty = 1,
     pch = 4,
     xlim = as.POSIXct(c("2019-08-08", "2019-08-09")),
     xlab = "Time (2019-08-08)", 
     ylab = "Temperature within hive", 
     main = "Sensor measurements",
     xaxt="n")
axis.POSIXct(1, 
             at=seq(min(df.4$time), max(df.4$time), by="1 hour"), 
             format="%H:00") # added this

# subset data
df.4 <- df[df$hive==4,]
# plot temperature outside
plot(df.4$time, df.4$t_o, ylim=c(0,40),type = 'p', pch=4)
# choose colours
cl <- rainbow(5)
# choose colums
cols <- 4:8
# plot each column
for (i in 1:5){
    lines(df.4$time, 
          df.4[,cols[i]],
          col = cl[i],
          type = 'p', 
          pch=4, 
          ylim=c(0,40))
}
# add legend
legend("topright", legend=c(1, 2, 3, 4, 5, "outside"),
       col=c(cl, "black"), pch = 4, lty = 0, cex=0.8)

# add legend
legend("topright", legend=c(1, 2, 3, 4, 5, "outside"),
       col=c(cl, "black"), pch = 4, lty = 0, cex=0.8)

# plot data
library(ggplot2)
ggplot(data = df.4, aes(x=time, y=t_i_3)) + geom_point()

# plot data
library(ggplot2)
ggplot(data = df.4, aes(x=time, y=t_i_3)) + geom_point(shape=4) + 
  ylim(c(0, 40)) + 
  xlab("Time (2019") + 
  ylab("Temperature within hive") + 
  ggtitle("Sensor measurements")

## Warning: Removed 152 rows containing missing values (geom_point).

# subset data
df.4 <- df[df$hive==4,]
# choose columns
df.4.cols <- df.4[,c(1,4:9)]
# reshape data
library(reshape)
mdf <- melt(df.4.cols, id=c("time")) 
# plot data
library(ggplot2)
ggplot(data = mdf, aes(x=time, y=value)) + 
  geom_line(aes(colour=variable)) + 
  ylim(c(0, 40))

library(plotly)
fig <- plot_ly(df.4[1:100,], x = ~time, y = ~t_i_3)
fig

png("test.png")
plot(hist(rnorm(100)))
dev.off()

library(magrittr)
x <- 9
# Calculate the square-root of x
sqrt(x)

## [1] 3

# Calculate it using pipes
x %>% sqrt

## [1] 3

x <- 9
# Calculate the square-root of x and update x
x <- sqrt(x)
x

## [1] 3

# Calculate it using pipes
x <- 9
x %<>% sqrt
x

## [1] 3

nrow(subset(df, hive==4))

## [1] 60193

df %>% subset(hive==4) %>% nrow

## [1] 60193

You can use it for basically everything.

https://rmarkdown.rstudio.com/gallery.html

• html
• pdf
• word
• …

# html
---
title: "R Markdown Example"
author: "Somebody"
date: "August 14, 2019"
output: html_document
---

# word
---
title: "R Markdown Example"
author: "Somebody"
date: "August 14, 2019"
output: word_document
---

---
title: "R Markdown Example"
author: "Somebody"
date: "August 14, 2019"
output: 
  html_document:
    toc: true # print a table of content
    toc_depth: 2 # maximal depth of the table of content
    number_sections: true # automatically number the sections
    code_folding: hide # have buttons to show/ hide code
    theme: united # specify a theme
    toc_float: true # generate a navigation bar

---

# Header 1

## Header 2

### Header 3

Use the insert button. You have following further options:

• eval: If set to true, the code will be executed
• echo: If set to true, the output will be printed/ plotted
• include: If set to true, the code will be printed.

* unordered list
    + sub-item 1
    + sub-item 2
        - sub-sub-item 1
* item 2

    
1. ordered list
2. item 2
    i) sub-item 1

• unordered list
  • sub-item 1
  • sub-item 2
    • sub-sub-item 1
• item 2

1. ordered list
2. item 2
   1. sub-item 1

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A good documentation of R-Markdown can be found here: https://bookdown.org/yihui/rmarkdown/

And here is a cheat sheet: https://www.rstudio.com/resources/cheatsheets/#rmarkdown

• Create an R-Markdown document
• Choose a dataset
• Think about the following question: What do you want fo find out? For a large assignment, you should write down a proper motivation for your analysis.
• Choose 3 of the following tasks for a small assignment and 8 for a large assignment. You can also come up with own ideas.
• Write a short text for each task explaining what you have been doing
• If you are doing the large assignment, make sure to write about the origin of your data, the motivation for the analysis and discuss your results

• Make some exploratory analysis:
• print the mean/ median/ standard deviation for each column
• print the total number of missing values
• print the number of missing values for each column
• Transform some of the columns/ Create new columns based on existing ones
• Create a subset of your data
• Exclude all rows with missing values
• Create a plot
• Write a function that takes a filename and some additional parameters. The function should create a plot and save it as “png” with that filename
• Create a linear model